{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97a9a76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x   y\n",
       "0  1   7\n",
       "1  2   9\n",
       "2  3  11\n",
       "3  4  13\n",
       "4  5  15"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取数据\n",
    "import pandas as pd\n",
    "data = pd.read_csv('generated_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad4567b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     2\n",
      "2     3\n",
      "3     4\n",
      "4     5\n",
      "5     6\n",
      "6     7\n",
      "7     8\n",
      "8     9\n",
      "9    10\n",
      "Name: x, dtype: int64 0     7\n",
      "1     9\n",
      "2    11\n",
      "3    13\n",
      "4    15\n",
      "5    17\n",
      "6    19\n",
      "7    21\n",
      "8    23\n",
      "9    25\n",
      "Name: y, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#data赋值\n",
    "x=data.loc[:,'x']\n",
    "y=data.loc[:,'y']\n",
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0d44ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#展示数据\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize = (10,10))\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f0557b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建线性回归模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr_model = LinearRegression()\n",
    "\n",
    "#sklearn需要2维数组\n",
    "#将[1,2,3,4]转为[[1,2,3,4]]\n",
    "import numpy as np\n",
    "x = np.array(x)\n",
    "x = x.reshape(-1,1)\n",
    "\n",
    "y = np.array(y)\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dee1cbb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#设置函数参数\n",
    "lr_model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "904b93db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.]\n",
      " [ 9.]\n",
      " [11.]\n",
      " [13.]\n",
      " [15.]\n",
      " [17.]\n",
      " [19.]\n",
      " [21.]\n",
      " [23.]\n",
      " [25.]]\n"
     ]
    }
   ],
   "source": [
    "#尝试获取预测值\n",
    "y_predict = lr_model.predict(x)\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93d88d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12.]]\n"
     ]
    }
   ],
   "source": [
    "#获取预测值\n",
    "y_3 = lr_model.predict([[3.5]])\n",
    "print(y_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fec5cab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.]] [5.]\n"
     ]
    }
   ],
   "source": [
    "#打印a,b\n",
    "a = lr_model.coef_\n",
    "b = lr_model.intercept_\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f926ab44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4909471108677122e-29 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "#判断均方误差（MSE），越小越好\n",
    "MSE = mean_squared_error(y,y_predict)\n",
    "#判断R2分数，越接近1越好\n",
    "R2 = r2_score(y,y_predict)\n",
    "print(MSE,R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83629421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#图像展示\n",
    "plt.figure()\n",
    "plt.plot(y,y_predict)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d304b5c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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